Automated translation among EPSILON languages for performance-driven UML software model refactoring

Although performance represents a crucial non-functional attribute of software, few model-based approaches have been introduced up today for reducing the gap between performance analysis results (e.g., mean response time) and the feedback expected by software engineers when performance problems are detected (i.e., refactoring actions). However, existing approaches aimed at performance-driven refactoring of software models suffer from fragmentation across different paradigms, languages, and meta-models. This paper aims at reducing such fragmentation by exploiting the EPSILON environment, which provides a suite of languages for checking properties and applying refactoring on models. In particular, we introduce automation aimed at translating performance antipattern detection rules and refactoring actions among three EPSILON languages. Such automation helps to reduce code writing effort, in the context of performance-driven refactoring of UML models, while exploiting the specific support provided by the different execution semantics of considered languages.